Quality of Service (QoS) of virtual machines (VMs) is guaranteed by the Service Level Agreements (SLAs) signed between users and service providers during the renting of VMs. A typical idea to ensure the SLAs being reached is to predict the QoS of VMs accurately and then take the appropriate measures according to the prediction results timely. However, the QoS is affected by multiple VM-related features, among which the uncertain and non-linear relationships are challenging to represent and analyze. Thus, in this paper, we construct a class parameter augmented Bayesian Network (CBN) to overcome the difficulties and then predict the QoS of VMs accurately. Specifically, we first cluster multiple VM-related features based on the Euclidean distance, and then use XGboost to classify the different VM configurations within each cluster. Then, we construct the CBN based on the classification results as well as the corresponding QoS values. Consequently, we predict the QoS of VMs via the variable elimination (VE) with CBN. Experimental results show the efficiency and effectiveness of our proposed method on predicting the QoS of VMs.

虚拟机（VM）的服务质量（QoS）由用户和服务提供商在VM租用期间签署的服务水平协议（SLA）来保证。确保达到SLA的典型想法是准确预测VM的QoS，然后根据预测结果及时采取适当的措施。但是，QoS受多个与VM相关的功能的影响，其中不确定性和非线性关系难以表示和分析。因此，在本文中，我们构造了一个类参数增强贝叶斯网络（CBN）来克服这些困难，然后准确地预测虚拟机的QoS。具体来说，我们首先根据欧几里得距离对多个与VM相关的功能进行聚类，然后使用XGboost对每个聚类中的不同VM配置进行分类。然后，我们根据分类结果以及相应的QoS值构造CBN。因此，我们通过使用CBN的变量消除（VE）来预测VM的QoS。实验结果表明，本文提出的方法可以有效地预测虚拟机的服务质量。